Choose the least costly, fastest model that meets your task’s quality and operational requirements on representative examples. Start by defining what the model must do, establish a capable baseline, and test smaller or specialized candidates against the same workload. If no smaller option clears your quality bar, use a more capable model, redesign the task, or split the workflow.
Start with the task, not the model’s size
A model is a candidate only if it supports the capabilities the task requires. Specify whether you need text classification, extraction, summarization, image input, tool use, or multistep reasoning before comparing speed or cost. A model that lacks a required modality or function is not a viable option, regardless of its size.
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Do not treat parameter count, a model-family label, or a public leaderboard position as a reliable predictor of your application’s results. Public benchmarks may represent different tasks and traffic patterns from yours. Use them to shortlist candidates, then evaluate those candidates on your own representative inputs. See AWS guidance on choosing models for generative AI applications and AWS’s guide to evaluating LLMs for specific tasks.
Define the quality bar before optimizing cost
Write down what an acceptable result means for this task. The bar should reflect the consequences of mistakes: an occasional formatting error may be tolerable in a draft, while an incorrect high-impact classification may not be. Choose the least costly or fastest candidate that passes the defined bar; do not assume the smallest model is automatically the right one.
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Write a task contract
Record the expected inputs and outputs, required capabilities, must-not-fail conditions, acceptable errors, and any deadline or deployment constraints. This turns a vague goal such as “answer support questions well” into criteria a team can actually test. Microsoft’s model-selection guidance recommends aligning selection with workload requirements and evaluating models accordingly.
Build a representative test set
Include ordinary examples, edge cases, and difficult cases where failure is costly. A handful of interactive demonstrations is not enough to establish reliability. Use the same inputs and task instructions for every candidate so differences in results are meaningful.
Score what matters
Assess correctness, completeness, relevance, and adherence to required format or instructions. If the model must call tools, measure whether those calls succeed. For subjective qualities, use human or model-assisted raters with a written rubric; fluent or confident wording is not evidence of correctness.
Also measure end-to-end latency and cost for the workflow. Include network time, preprocessing and postprocessing, retries, fallback calls, and any human review that the workflow requires. A cheaper first call can cost more overall if it often needs correction or escalation.
Compare candidates on the same practical criteria
| Comparison area | What to check |
|---|---|
| Task capability | Required modality, tool or function support, domain fit, and reasoning demands. |
| Quality | Correctness, completeness, relevance, format and instruction adherence, and severity of errors. |
| Latency | Median and tail response time under realistic network and processing conditions, compared with the user-facing deadline. |
| Cost | Cost per request or completed task at realistic input and output volumes, including retries and fallback calls. Current pricing varies by provider and model; there is no single cross-provider price comparison here. |
| Context and deployment | Whether the context window fits the workload, and whether region, data, and deployment requirements can be met. |
| Maintainability | Whether assignments can be monitored, changed, and rolled back as models, traffic, or requirements evolve. |
Judge response time from the user’s perspective, not just by the model’s generation speed. A real-time interaction may have a tighter deadline than an asynchronous analysis job. AWS gives sub-second response as an example for autocomplete or voice; that is an example, not a universal latency target. See AWS’s model-selection guidance for its discussion of task needs and response time.
Run the comparison in a repeatable sequence
- Set the acceptance bar. Define minimum quality and operational requirements from the task contract before looking for the lowest-cost option.
- Choose a capable baseline. Test a candidate likely to handle the task well, then use its results as a point of comparison.
- Test smaller or specialized candidates. Run them on the same examples, instructions, and conditions. AWS recommends testing smaller variants early to understand how quality changes as you move to them.
- Measure the whole workflow. Score output quality, format validity, tool success where relevant, end-to-end latency, and total cost per completed task.
- Select only a passing candidate. If no smaller option meets the bar, use a more capable or specialized model, revise the task design, or divide the work into stages.
- Monitor production results. Track quality, latency, token use or cost, and fallback rates by task class. Reevaluate when traffic, requirements, or model versions change.
AWS’s Well-Architected guidance on task-appropriate model selection and Microsoft’s model-selection guidance both emphasize evaluating choices against the workload rather than treating selection as a one-time decision.
When a smaller or larger model may fit
Consider a smaller model for well-defined work
Routine classification, extraction, and other bounded tasks are reasonable candidates for a smaller or faster model when tests show it meets your quality bar. Simpler work does not guarantee success, however: measure the actual outputs rather than inferring performance from the task label.
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Use a more capable option when the task warrants it
Ambiguous requests, dependent steps, or work where errors carry a high cost may justify a more capable or reasoning-oriented option. These are tendencies, not guarantees based on size or a model-family name alone.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use routing and fallback for mixed-difficulty workloads
If requests vary in difficulty, classify them into task groups and assign each group a model tier that has passed evaluation. A smaller model can handle a well-defined class while a more capable option handles complex or uncertain cases. Make escalation rules explicit—for example, escalate an invalid, incomplete, or low-confidence result—and measure whether the fallback recovers quality at acceptable latency and cost.
Do not blindly repeat the same call when a result fails. A retry may be useful for a transient error, but an inadequate answer may call for a more capable model, a revised prompt, or human review. Track quality and fallback rates by task class so you can see where routing helps and where it adds overhead. AWS’s model-selection guidance recommends monitoring per-class performance and revisiting assignments.
Know the trade-offs of a router
Runtime routing is useful when request characteristics or workload needs vary, but it adds a decision layer that also needs evaluation and monitoring. Microsoft notes that a router’s choices are constrained by its model pool and can limit effective context length to the smallest candidate window. When requirements are stable, selecting a model at design time may be simpler. In either case, keep assignments traceable so you can identify which model handled a request. See Microsoft Learn’s model-selection guidance.
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Model offerings, versions, traffic, and application requirements can change. Keep model assignments configurable where practical, preserve a representative evaluation set, and rerun comparisons when a candidate or workload changes. In production, review quality, latency, cost or token use, and fallback rates by task class—not only aggregate averages, which can conceal a weak class of requests.
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